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Intelligent control algorithm for ship dynamic positioning

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Ship motion in the sea is a complex nonlinear kinematics. The hydrodynamic coefficients of ship model are very difficult to accurately determine. Establishing accurate mathematical model of ship motion is difficult because of changing random factors in the marine environment. Aiming at seeking a method of control to realize ship positioning, intelligent control algorithms are adopt utilizing operator's experience. Fuzzy controller and the neural network controller are respectively designed. Through simulations and experiments, intelligent control algorithm can deal with the complex nonlinear motion, and has good robustness. The ship dynamic positioning system with neural network control has high positioning accuracy and performance.
Rocznik
Strony
479--497
Opis fizyczny
Bibliogr. 23 poz., rys., tab.
Twórcy
autor
  • Wuhan University of Technology, Mechanic and Electronic Engineering,Wuhan, China, 731768832@qq.com
autor
  • Wuhan University of Technology, Mechanic and Electronic Engineering,Wuhan, China
autor
  • Wuhan University of Technology, Mechanic and Electronic Engineering,Wuhan, China
autor
  • Wuhan University of Technology, Mechanic and Electronic Engineering,Wuhan, China
Bibliografia
  • [1] A. J. Sorensen: A survey of dynamic positioning control systems. Annual Reviews in Control, 35(1), (2011), 123-136.
  • [2] Yang Yang, Jialu Du, Guangqiang Li, Wenhua Li and Chen Guo: Dynamic surface control for nonlinear dynamic positioning system of ship. Mechanical Engineering and Technology, 125 (2012), 237-244.
  • [3] E. A. Tannuri, H. M. Morishita: Experimental and numerical evaluation of a typical dynamic positioning system. Applied Ocean Research, 28 (2006), 133-146.
  • [4] Van Phuoc Bui, Sang Won Ji, Kwang Hwan Choi and Young Bok Kim: Nonlinear observer and sliding mode control design for dynamic positioning of a surface vessel. Int. Conf. on Control, Automation and Systems, Jeju Island, (2012), 1900-1904.
  • [5] N. E. Kahveci, A. Petros and A. Ioannou: Adaptive steering control for uncertain ship dynamics and stabilityanalysis. Automatica, 49 (2013), 685-697.
  • [6] P. Martin and R. Katebi: Multivariable PID tuning of dynamic ship positioning control systems. J. of Marine Engineering and Technology,1 (2005), 11-24.
  • [7] N. Akasaka: Design of piecewise linear LQ control by multivariable circle criterion for dynamic positioning system. SICE-ICASE Int. Joint Conf., Busan, Korea, (2006), 5672-5677.
  • [8] Wang Liyun, Xiao Jianmei and Wang Xihuai: Ship dynamic positioning systems based on fuzzy predictive control. Telkomnika, 11 (2013), 6769-6779.
  • [9] E. W. Mcgookin, D. J. Murray-Smith, Y. Li and T. I. Fossen: Ship steering control system optimisation using genetic algorithms. Control Engineering Practice, 8 (2000), 429-443.
  • [10] Zhang Cheng-Du, Wang Xi-Huai and Xiao Jian-Mei: Ship dynamic positioning system based on backstepping control. J. of Theoretical and Applied Information Technology, 51(1), (2013), 129-136.
  • [11] J. Fotakis, M. J. Grimble and B. Kouvaritakis: A comparison of characteristic locus and optimal designs for dynamic ship positioning systems. IEEE Trans. on Automatic Control, 27(6), (1982), 1143-1157.
  • [12] T. I. Fossen: A survey on nonlinear ship control: from theory to practice. Proc. of the 5th IFAC Conf. on Manoeuvring and Control of Marine Craft, Aalborg, Denmark, (2000).
  • [13] M. Parnichkun and Ch. Ngaecharoenkul: Kinematics control of a pneumatic system by hybrid fuzzy PID. Mechatronics, 11 (2001), 1001-1023.
  • [14] Yong Wang and Bao-Zhu Jia: Fuzzy switching PID controller for ship motion. Navigation of China, 4 (2006), 30-34.
  • [15] Jia Baozhu, Guang Ren and Zhihong Xiu: Fuzzy Switching Controller for Multiple Model. Second Int. Conf. on Fuzzy Systems and Knowledge Discovery, Changsha, China, 1 (2005), 1011- 1014.
  • [16] Jialu Du, Yang Yang, Dianhui Wang and Chen Guo: A robust adaptive neural networks controller for maritime dynamic positioning system. Neurocomputing, 110 (2013), 128-136.
  • [17] A. Boulkroune, N. Bounar, M. M’saad and M. Farza: Indirect adaptive fuzzy control scheme based on observer for nonlinear systems: A novel SPR-filter approach. Neurocomputing, 135 (2014), 378-387.
  • [18] Dong W. Kim, Nak-Hyun Kim and Gwi-Tae Park: Neural network control of humanoid robot. J. of Institute of Control, Robotics and Systems, 16(10), (2010), 963-968.
  • [19] T. D. Nguyen, A. J. Sfirensen, S. T. Quek: Design of hybrid controller for dynamic positioning from calm to extreme sea conditions. Automatica, 43 (2007), 768-785.
  • [20] T. I. Fossen: Marine Control Systems: Guidance, Navigation and Control of Ships, Rigs and Underwater Vehicles. Marine Cybernetics, Trondheim, Norway. 2002.
  • [21] Yao-Qing Ren, Xiao-Gang Duan, Han-Xiong Li and C.L. Philip Chen: Dynamic switching based fuzzy control strategy for a class of distributed parameter system. Journal of Process Control, 24 (2014), 88-97.
  • [22] S. Chekkal, N. A. Lahacani, D. Aouzellag, K. Ghedamsi: Fuzzy logic control strategy of wind generator based on the dual-stator induction generator. Electrical Power and Energy Systems, 59 (2014), 166-175
  • [23] T-C. M, J. L Siva Neto and H. Le-Huy: Fuzzy logic based controller for induction motor drives. Proc. of the Canadian Conf. on Electrical and Computer Engineering, 2 (1996), 631-634
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8e49e6b8-5709-4773-bb4a-2bfd73d5757e
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